Anderson, Joshua2021-09-242021-09-242019-07https://hdl.handle.net/11299/224496University of Minnesota M.S. thesis. July 2019. Major: Applied Plant Sciences. Advisors: James Luby, Cindy Tong. 1 computer file (PDF); ix, 209 pages.Current phenotyping methods for apple (Malus × domestica Borkh.) fruit and trees are destructive, time and labor intensive and can be subjective. Fruit: A handheld NIR spectrometer was used to collect spectra along with traditional phenotyping of several fruit quality traits. Two trait spectral models (starch pattern index, and soluble solids concentration) had sufficient predictive ability across 15 cultivars. Temperature and outdoor limitations of the spectrometer were minimal compared to the importance of collecting more than a single scan per fruit to control for individual fruit heterogeneity. Trees: A low-cost RGB-D sensor was used to characterize trees of a rootstock experiment. The relationship between image-derived metrics and hand-measured was highest for tree height R2=0.93, and TCA R2=0.71. The predictive ability of cumulative yield by image based-tree volume was lower than by manual-measured tree volume. Tree volume, in general, did not improve upon other mixed models when estimating cumulative yield.enApple fruit qualityImagingNear infrared spectroscopyPortableTree architectureYield efficiencyThesis or Dissertation